Method, device, and computer program product for processing data

ABSTRACT

Embodiments of the present disclosure relate to a method, a device, and a computer program product for processing data. The method includes: loading, at a switch and in response to receipt of a model loading request from a terminal device, a data processing model specified in the model loading request. The method further includes: acquiring model parameters of the data processing model from the terminal device. The method further includes: processing, in response to receipt of to-be-processed data from the terminal device, the data using the data processing model based on the model parameters. Through the method, data may be processed at a switch, which improves the efficiency of data processing and the utilization rate of computing resources, and reduces the delay of data processing.

RELATED APPLICATION(S)

The present application claims priority to Chinese Patent ApplicationNo. 202010367250.4, filed Apr. 30, 2020, and entitled “Method, Device,and Computer Program Product for Processing Data,” which is incorporatedby reference herein in its entirety.

FIELD

Embodiments of the present disclosure relate to the field of dataprocessing, and more particularly, to a method, a device, and a computerprogram product for processing data.

BACKGROUND

With the development of computer technologies, the concept of Internetof Things (IoT) has been proposed. IoT is an extended and expandednetwork on the basis of the Internet, which combines various informationsensing devices with the Internet to form a huge network for achievinginterconnection of people, machines, and things at any time and anyplace.

In addition, with the development of big data technologies, a neuralnetwork model has been proposed and used. A neural network model is acomplex network system formed by a large number of simple processingunits that are interconnected widely. Because a neural network model canquickly process a large amount of data, some data in the IoT is alsoprocessed using a neural network model at present. However, there arestill many problems to be solved in application of a neural networkmodel in the IoT.

SUMMARY

The embodiments of the present disclosure provide a method, a device,and a computer program product for processing data.

According to a first aspect of the present disclosure, a method forprocessing data is provided. The method includes: loading, at a switchand in response to receipt of a model loading request from a terminaldevice, a data processing model specified in the model loading request.The method further includes: acquiring model parameters of the dataprocessing model from the terminal device. The method further includes:processing, in response to receipt of to-be-processed data from theterminal device, the data using the data processing model based on themodel parameters.

According to a second aspect of the present disclosure, a method forprocessing data is provided. The method includes: sending, at a terminaldevice, a model loading request to a switch, a data processing model tobe loaded by the switch being specified in the model loading request.The method further includes: sending model parameters of the dataprocessing model to the switch. The method further includes: sendingto-be-processed data to the switch, such that the to-be-processed datais processed at the switch based on the data processing model.

According to a third aspect of the present disclosure, a switch isprovided. The switch includes: a processor; and a memory storingcomputer program instructions. The processor runs the computer programinstructions in the memory to control an electronic device to performactions. The actions include: loading, in response to receipt of a modelloading request from a terminal device, a data processing modelspecified in the model loading request; acquiring model parameters ofthe data processing model from the terminal device; and processing, inresponse to receipt of to-be-processed data from the terminal device,the data using the data processing model based on the model parameters.

According to a fourth aspect of the present disclosure, a terminaldevice is provided. The terminal device includes: a processor; and amemory storing computer program instructions. The processor runs thecomputer program instructions in the memory to control an electronicdevice to perform actions. The actions include: sending a model loadingrequest to a switch, a data processing model to be loaded by the switchbeing specified in the model loading request; sending model parametersof the data processing model to the switch; and sending to-be-processeddata to the switch, such that the to-be-processed data is processed atthe switch based on the data processing model.

According to a fifth aspect of the present disclosure, a computerprogram product is provided. The computer program product is tangiblystored on a non-volatile computer-readable medium and includesmachine-executable instructions that, when executed, cause a machine toperform steps of the method in the first aspect of the presentdisclosure.

According to a sixth aspect of the present disclosure, a computerprogram product is provided. The computer program product is tangiblystored on a non-volatile computer-readable medium and includesmachine-executable instructions that, when executed, cause a machine toperform steps of the method in the first aspect of the presentdisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objectives, features, and advantages of the presentdisclosure will become more apparent by the following detaileddescription of example embodiments of the present disclosure, presentedwith reference to the accompanying drawings, and in the exampleembodiments of the present disclosure, the same reference numeralsgenerally represent the same components.

FIG. 1 illustrates a schematic diagram of example environment 100 inwhich a device and/or a method according to embodiments of the presentdisclosure may be implemented;

FIG. 2 illustrates a schematic diagram of switch structure 200 accordingto an embodiment of the present disclosure;

FIG. 3 shows a flowchart of method 300 for processing data according toan embodiment of the present disclosure;

FIG. 4 shows a flowchart of method 400 for processing data according toan embodiment of the present disclosure;

FIG. 5 illustrates a schematic diagram of process 500 of data exchangebetween a terminal device and a switch according to an embodiment of thepresent disclosure;

FIG. 6 illustrates a schematic diagram of neural network model 600according to an embodiment of the present disclosure;

FIG. 7A illustrates a schematic diagram of process 700-1 of loading adata processing model and model parameters according to an embodiment ofthe present disclosure;

FIG. 7B illustrates a schematic diagram of data processing process 700-2according to an embodiment of the present disclosure;

FIG. 8 illustrates a schematic diagram of process 800 for processing aparameter packet according to an embodiment of the present disclosure;

FIG. 9 illustrates a schematic diagram of process 900 for processing adata packet according to an embodiment of the present disclosure; and

FIG. 10 illustrates a schematic block diagram of example device 1000that is adapted to implement embodiments of the present disclosure.

In each figure, the same or corresponding reference numerals representthe same or corresponding parts.

DETAILED DESCRIPTION

The embodiments of the present disclosure will be described in moredetail below with reference to the accompanying drawings. Although someembodiments of the present disclosure are illustrated in theaccompanying drawings, it should be understood that the presentdisclosure may be implemented in various forms and should not beconstrued as being limited to the embodiments set forth herein. Rather,these embodiments are provided for a more thorough and completeunderstanding of the present disclosure. It should be understood thatthe accompanying drawings and embodiments of the present disclosure arefor illustrative purposes only, and are not intended to limit the scopeof protection of the present disclosure.

In the description of the embodiments of the present disclosure, theterm “include” and similar terms should be understood as open inclusion,i.e., “including but not limited to.” The term “based on” should beunderstood as “based at least in part on.” The term “one embodiment” or“this embodiment” should be understood as “at least one embodiment.” Theterms “first,” “second,” etc. may refer to different or identicalobjects. Other explicit and implicit definitions may also be includedbelow.

The principles of the present disclosure will be described below withreference to several example embodiments shown in the accompanyingdrawings. Although preferred embodiments of the present disclosure areillustrated in the accompanying drawings, it should be understood thatthese embodiments are described only to enable those skilled in the artto better understand and thus implement the present disclosure, and donot limit the scope of the present disclosure in any way.

With the increased use of IoT devices, IoT devices will generateincreasingly large amounts of information. Some of the information needsto be processed through a data processing model. For example, data inIoT devices is processed through a neural network model. However, thedesign of most conventional IoT devices does not support data processingmodel algorithms, such as a neural network model algorithm. For example,the functions of central processing units (CPUs) of IoT devices are notpowerful enough, and there is no graphics processing unit (GPU) ortensor processing unit (TPU). Therefore, IoT devices send data to a datacenter server for data processing with a neural network model, and theresult is returned to IoT devices. However, this process results in along delay.

In addition, data processing with a data processing model in a server orIoT device will consume CPU, GPU, and memory resources, take up largeamounts of computing resources, and reduce the processing efficiency ofthe server or the IoT device.

In order to solve the above problem, the present disclosure provides amethod for processing data. In the method, at a switch and in responseto receipt of a model loading request from a terminal device, a dataprocessing model specified in the model loading request is loaded. Then,model parameters of the data processing model are acquired from theterminal device. After receiving to-be-processed data from the terminaldevice, the data is processed using the data processing model based onthe model parameters. Through the method, data may be processed at aswitch, which improves the efficiency of data processing and theutilization rate of computing resources, and reduces the delay of dataprocessing.

Hereinafter, FIG. 1 illustrates a schematic diagram of exampleenvironment 100 in which a device and/or a method according toembodiments of the present disclosure may be implemented.

As shown in FIG. 1, a switch 102 and multiple different terminal devices104-1, 104-2, 104-3, . . . , 104-N are included in the exampleenvironment, where N is a positive integer. Terminal devices 104-1,104-2, 104-3, . . . , 104-N are collectively referred to herein asterminal device 104.

Terminal device 104 may be implemented as any type of computing device,including, but not limited to, a mobile phone (for example, smartphone), a laptop computer, a portable digital assistant (PDA), anelectronic book (e-book) reader, a portable game machine, a portablemedia player, a game machine, a set-top box (STB), a smart television(TV), a personal computer, a laptop computer, an on-board computer (forexample, navigation unit), a camera, a surveillance camera, a printer, ascanner, a smart door lock, and other various IoT devices.

Terminal device 104 may send data to switch 102. Switch 102 may processdata from terminal device 104 or may forward the data to cloud 106, forexample, to other servers in cloud 106.

Various data processing models may be run in switch 102 to processvarious data from terminal device 104. In some embodiments, the dataprocessing model is a machine learning model. Alternatively oradditionally, the machine learning model is a neural network model, suchas a convolutional neural network model. In some embodiments, the dataprocessing model is any suitable model that may process data. In someembodiments, switch 102 is a programmable switch.

When running the data processing model, switch 102 also needs to receivemodel parameters and to-be-processed data from terminal device 104.Then, the data is processed using the data processing model based on thereceived model parameters. A data processing result may be returned toterminal device 104 or transmitted to a destination device based on adestination address transmitted from terminal device 104.

In some embodiments, switch 102 includes a programmable switch.Alternatively or additionally, switch 102 is a switch programmable in P4language.

A schematic diagram of environment 100 in which a device and/or a methodaccording to embodiments of the present disclosure may be implemented isdescribed above in conjunction with FIG. 1. An example switch accordingto an embodiment of the present disclosure is described below inconjunction with FIG. 2. FIG. 2 illustrates a schematic diagram ofswitch structure 200 according to an embodiment of the presentdisclosure.

As shown in FIG. 2, switch 102 includes processor 206 and programmablechip 204. Processor 206 is configured to manage the operation of switch102. Programmable chip 204 may be configured to process data or requestsreceived from terminal device 104. In some embodiments, a dataprocessing model, such as a neural network model, may be loaded onprogrammable chip 204. Programmable chip 204 may also recognize a packetincluding model parameters and a packet including to-be-processed datafrom packets received from terminal device 104. Then, programmable chip204 processes data using the obtained parameters and the loaded dataprocessing model.

An example of switch 102 according to an embodiment of the presentdisclosure is described above in conjunction with FIG. 2. A process ofprocessing data at switch 102 is described below in conjunction withFIG. 3. FIG. 3 shows a flowchart of method 300 for processing dataaccording to an embodiment of the present disclosure. Method 300 may beperformed at switch 102 in FIG. 1 or at any other suitable device.

At block 302, switch 102 loads, in response to receipt of a modelloading request from terminal device 104, a data processing modelspecified in the model loading request. Switch 102 performs a modelloading operation after receiving the loading request.

In some embodiments, the data processing model is a machine learningmodel. Alternatively or additionally, the machine learning model is aneural network model. In some embodiments, the data processing model isanother suitable model for processing data.

In some embodiments, terminal device 104 may first send a request foracquiring a service list to switch 102 to ask what kind of servicesswitch 102 can provide, for example, what kind of data processing modelsmay be executed. Switch 102 then sends a service list to terminal device104, such as a list of operable data processing models. Terminal device104 then requests to load a data processing model available in switch102. In some embodiments, terminal device 104 directly sends a requestfor loading a data processing model to switch 102. The above examplesare only for describing the present disclosure, rather than specificallylimiting the present disclosure.

In some embodiments, switch 102 acquires an identifier of a dataprocessing model in the model loading request. Then, switch 102 selects,based on the identifier, a data processing model from a predetermineddata processing model set. After obtaining the data processing model,the selected data processing model is loaded.

In some embodiments, switch 102 searches for a locally stored dataprocessing model. If it exists, the data processing model is directlyloaded. If it does not exist, a data processing model is acquired fromother devices.

In some embodiments, after loading the data processing model, switch 102will send a response of successful model loading to terminal device 104.

At block 304, switch 102 acquires model parameters of the dataprocessing model from terminal device 104. In order to run the dataprocessing model, it is also necessary to acquire model parameters forrunning the model from terminal device 104.

In some embodiments, when generating a packet to be sent to switch 102,terminal device 104 will set a content label in the packet to indicatethe content of the packet. When receiving the packet, switch 102 willdetect the label in the packet. Then, it is determined according to thelabel whether the packet includes model parameters related to the dataprocessing model. If the label included in the packet indicates that thepacket includes model parameters, switch 102 will acquire the modelparameters from the packet.

In some embodiments, the data processing model is a neural networkmodel. Switch 102 determines a parameter size corresponding to eachprocessing layer of the neural network model. In one example, since theneural network model is fixed, parameters corresponding to eachprocessing layer of the neural network model may be determined based onthe neural network model. In one example, a size of parameters of eachprocessing layer is identified from a parameter packet received fromterminal device 104. Switch 102 acquires model parameters correspondingto each processing layer from the packet according to the parametersize.

In some embodiments, after the model parameters have been successfullyacquired, switch 102 may send a response to terminal device 104 toindicate that the model parameters have been successfully acquired byswitch 102.

At block 306, switch 102 determines whether to-be-processed data fromterminal device 104 is received. Upon receiving to-be-processed datafrom terminal device 104, at block 308, switch 102 processes the datausing the data processing model based on the model parameters. A dataprocessing result is obtained after the data is processed by the neuralnetwork model.

In some embodiments, switch 102 will detect a label of the receivedpacket. Switch 102 determines whether the packet includes data to beprocessed by the data processing model through the label of the packet.If the label of the packet indicates that the packet includes data to beprocessed by the data processing model, switch 102 determines that theto-be-processed data is received.

In some embodiments, the data processing model is a neural networkmodel. Switch 102 transmits the received data to the neural networkmodel as an input. Then, switch 102 processes, based on model parameterscorresponding to each processing layer of the neural network model, thedata using the neural network model.

In some embodiments, switch 102 will receive a destination address fromterminal device 104, for example, a destination address set in thereceived packet including parameters or including data. Switch 102 sendsthe data processing result to the destination address. In someembodiments, switch 102 sends a processing result for the data toterminal device 104.

Through the above method, data may be processed at a switch, therebyimproving the efficiency of data processing and the utilization rate ofcomputing resources, and reducing the delay of data processing.

The process for processing data at switch 102 according to an embodimentof the present disclosure is described above in conjunction with FIG. 3.A process of processing data at terminal device 104 is described belowin conjunction with FIG. 4. FIG. 4 shows a flowchart of method 400 forprocessing data according to an embodiment of the present disclosure.Method 400 in FIG. 4 may be performed by terminal device 104 in FIG. 1or by any other suitable device.

At block 402, terminal device 104 sends a model loading request toswitch 102. A data processing model to be loaded by switch 102 isspecified in the model loading request. When intending to process datausing a data processing model, terminal device 104 may process data interminal device 104 using the data processing model at switch 102.

In some embodiments, terminal device 104 includes an identifier of thedata processing model in the model loading request sent to switch 102.Alternatively or additionally, before sending a model loading request,terminal device 104 will send a request to switch 102 to search forservices provided by switch 102, and then receive from switch 102 a listof services switch 102 can provide, for example, a list of dataprocessing models that may be provided.

At block 404, terminal device 104 sends model parameters of the dataprocessing model to switch 102. Since the data processing model loadedin switch 102 is determined, it is necessary to transmit modelparameters to the switch.

In some embodiments, terminal device 104 includes the model parametersin a packet to be sent. Then, terminal device 104 sets a content labelof the packet as indicating that the packet includes model parameters.Then, terminal device 104 sends the packet to switch 102.

In some embodiments, terminal device 104 will receive from switch 102 aresponse to the model loading request, and after receiving the response,terminal device 104 sends the model parameters to switch 102.

At block 406, terminal device 104 sends to-be-processed data to switch102, such that the to-be-processed data is processed at switch 102 basedon the data processing model. After sending the model parameters,terminal device 104 also sends to-be-processed data to switch 102 forprocessing by switch 102.

In some embodiments, terminal device 104 includes data to be processedby the data processing model in a packet to be sent. Then, terminaldevice 104 sets a content label of the packet as indicating that thepacket includes to-be-processed data. After the settings are completed,terminal device 104 sends the packet to switch 102.

In some embodiments, terminal device 104 sends the to-be-processed datato switch 102 after receiving from switch 102 a response indicating thatthe model parameters are successfully obtained. In some embodiments,terminal device 104 receives a processing result for the data fromswitch 102.

Through the above method, a terminal device may quickly obtain a dataprocessing result, thereby reducing the delay of obtaining a result andimproving the computing efficiency. FIG. 4 shows a schematic diagram ofmethod 400 for processing data according to an embodiment of the presentdisclosure above. An example operation of transferring data betweenterminal device 104 and switch 102 will be described below inconjunction with FIG. 5. FIG. 5 illustrates a schematic diagram ofprocess 500 of exchanging data between terminal device 104 and switch102 according to an embodiment of the present disclosure.

In FIG. 5, terminal device 104 first sends 502 a request for acquiring aservice list to switch 102. Then, switch 102 sends 504 service list toterminal device 104. For example, the service list may include variousneural network models that switch 102 can provide. After receiving theservice list and determining an available data processing model,terminal device 104 sends 506 a model loading request to switch 102.

Switch 102 then loads the data processing model based on a dataprocessing model identifier in the model loading request. Alternativelyor additionally, after loading the data processing model, switch 102sends a response of successful model loading to terminal device 104.Terminal device 104 then sends 508 model parameters to switch 102. Afterreceiving the model parameters, the switch acquires the modelparameters. When the data processing model is a neural network model,parameters of each processing layer are acquired according to a size ofeach processing layer of the neural network model.

Optionally, after switch 102 successfully receives the model parameters,switch 102 may send a response of successful parameter acquisition toterminal device 104. Terminal device 104 then sends 510 to-be-processeddata. Then, switch 102 processes data using the data processing model.Then, a processing result is returned 512 to terminal device 104.

By transferring data between a terminal device and a switch to processdata on the switch, the delay of data processing is reduced and theefficiency of data processing is improved.

FIG. 5 describes a schematic diagram of process 500 of exchanging databetween terminal device 104 and switch 102 according to an embodiment ofthe present disclosure above. Specific examples of the operation of theneural network model will be described below in conjunction with FIGS.6, 7A, and 7B. FIG. 6 illustrates a schematic diagram of neural networkmodel 600 according to an embodiment of the present disclosure. FIG. 7Aillustrates a schematic diagram of process 700-1 of loading a dataprocessing model and model parameters according to an embodiment of thepresent disclosure. FIG. 7B illustrates a schematic diagram of process700-2 of processing data according to an embodiment of the presentdisclosure.

Neural network model 600 shown in FIG. 6 has input layer 602,convolutional layer 604, fully-connected layer 606, loss function layer608, and output layer 610. Data to be processed is input by input layer602, a convolutional operation is then performed at convolutional layer604, and a generated intermediate result is input to fully-connectedlayer 606. For convenience of description, convolutional layer 604 iscalled a first processing layer, and fully-connected layer 606 is calleda second processing layer. The data computed by fully-connected layer606 is input to loss function layer 608. For example, the loss functionis softmax. The data is then output from output layer 610.

FIG. 7A describes the process of loading a neural network model andmodel parameters in FIG. 6. Switch 102 preferably performs a modelloading operation at block 702. Then, after receiving a data packet,switch 102 stores parameters of the first processing layer at block 704based on a size of parameters of the first processing layer in neuralnetwork model 600. The parameters of the first processing layer aresaved in parameter storage area 710. Switch 102 then stores parametersof the second processing layer based on a size of parameters of thesecond processing layer at block 706. The parameters of the secondprocessing layer are also stored in parameter storage area 710. Asuccess response is sent at block 708.

FIG. 7B describes the data processing process of neural network model600 in FIG. 6. Switch 102 loads data to be processed at block 712 afterreceiving a data packet. Switch 102 then acquires, from parameterstorage area 710, parameters corresponding to a first processing layer,such as parameters corresponding to convolutional layer 604, andperforms first processing layer computation on the received data atblock 714. When performing a convolutional operation, convolutionalparameters may be used to cyclically process different data sub-portionsin the data. Intermediate data obtained after processing by the firstprocessing layer is transmitted to intermediate data storage area 724.Then, second processing layer computation is performed at block 716. Atthis moment, parameters of a second processing layer, such as parametersof a fully-connected layer, are obtained from parameter storage area710. A computing result is then put into intermediate data storage area724. At block 718, intermediate data obtained from the second processinglayer computation is subjected to loss function processing, for example,using softmax. Then, processing result 720 is obtained.

The processes of loading parameters of neural network model 600 andprocessing data are described above in conjunction with FIGS. 6, 7A, and7B. The process of operating in a programmable chip of switch 102 isdescribed below with reference to FIGS. 8 and 9. FIG. 8 illustrates aschematic diagram of process 800 for processing a parameter packetaccording to an embodiment of the present disclosure. FIG. 9 illustratesa schematic diagram of process 900 for processing a data packetaccording to an embodiment of the present disclosure.

In FIG. 8, parameter packet 802 is transmitted to interface 804 of aprogrammable switch chip in switch 102, and then is parsed in inletparser 806. The parsed data is processed in inlet 808, and parameters ofa first processing layer are acquired from parameter packet 802. Theremaining data is then transmitted to outlet 810. If the parameterpacket further includes unobtained parameters, the parameter packet istransmitted from outlet 810 to interface 804 to further acquireparameters of a second layer or other layers. For example, a recirculatecommand in a programmable switch may be used to transmit a packetincluding unobtained parameters from outlet 810 back to interface 804.

In FIG. 9, data packet 902 is transmitted to interface 804 of aprogrammable switch chip in switch 102, and then is parsed in inletparser 806. The processed data is processed in inlet 808. For example, apart of the data is processed by convolutional processing, and then thedata is transmitted from inlet 808 back to interface 804, so as toacquire other data to be processed for convolutional operations. Forexample, the above process may be implemented through a resubmit commandin the programmable switch. The data transmission process is performeduntil the operations of all processing layers are completed, and thenthe data is output to outlet 810 to output a computing result.

FIG. 10 illustrates a schematic block diagram of example device 1000that may be used to implement embodiments of the present disclosure. Forexample, switch 102 and terminal device 104 as shown in FIG. 1 may beimplemented by device 1000. As shown in the figure, device 1000 includesCPU 1001 that may perform various appropriate actions and processingaccording to computer program instructions stored in read-only memory(ROM) 1002 or computer program instructions loaded from storage unit1008 to random access memory (RAM) 1003. In RAM 1003, various programsand data required for the operation of device 1000 may also be stored.CPU 1001, ROM 1002, and RAM 1003 are connected to each other through bus1004. Input/output (I/O) interface 1005 is also connected to bus 1004.

Multiple components in device 1000 are connected to I/O interface 1005,including: input unit 1006, such as a keyboard or a mouse; output unit1007, such as various types of displays or speakers; storage unit 1008,such as a magnetic disk or an optical disk; and communication unit 1009,such as a network card, a modem, or a wireless communicationtransceiver. Communication unit 1009 allows device 1000 to exchangeinformation/data with other devices over a computer network such as theInternet and/or various telecommunication networks.

The various processes and processing described above, such as method 300and method 400, may be performed by processing unit 1001. For example,in some embodiments, method 300 and method 400 may be implemented as acomputer software program that is tangibly included in amachine-readable medium, such as storage unit 1008. In some embodiments,some or all of the computer program may be loaded and/or installed ontodevice 1000 via ROM 1002 and/or communication unit 1009. One or moreactions of method 300 and method 400 described above may be performedwhen the computer program is loaded into RAM 1003 and executed by CPU1001.

Embodiments of the present disclosure include a method, an apparatus, asystem, and/or a computer program product. The computer program productmay include a computer-readable storage medium having computer-readableprogram instructions for performing various aspects of the presentdisclosure loaded thereon.

The computer-readable storage medium may be a tangible device that mayhold and store instructions used by an instruction-executing device. Forexample, the computer-readable storage medium may be, but is not limitedto, an electrical storage device, a magnetic storage device, an opticalstorage device, an electromagnetic storage device, a semiconductorstorage device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer-readablestorage medium include: a portable computer disk, a hard disk, RAM, ROM,an erasable programmable read only memory (EPROM or flash memory), astatic random access memory (SRAM), a portable compact disk read onlymemory (CD-ROM), a digital versatile disk (DVD), a memory stick, afloppy disk, a mechanical coding device such as a punch card or aprotruding structure within a groove having instructions stored thereon,and any suitable combination of the foregoing. The computer-readablestorage medium as used herein is not to be interpreted as transientsignals per se, such as radio waves or other freely propagatedelectromagnetic waves, electromagnetic waves propagated throughwaveguides or other transmission media (e.g., light pulses throughfiber-optic cables), or electrical signals transmitted throughelectrical wires.

The computer-readable program instructions described herein may bedownloaded from the computer-readable storage medium to variouscomputing/processing devices or downloaded to an external computer or anexternal storage device over a network, such as the Internet, a localarea network (LAN), a wide area network (WAN), and/or a wirelessnetwork. The network may include copper transmission cables, fiber optictransmission, wireless transmission, routers, firewalls, switches,gateway computers, and/or edge servers. A network adapter card ornetwork interface in each computing/processing device receives acomputer-readable program instruction from the network and forwards thecomputer-readable program instruction for storage in thecomputer-readable storage medium in each computing/processing device.

The computer program instructions for performing the operations of thepresent disclosure may be assembly instructions, instruction setarchitecture (ISA) instructions, machine instructions, machine-relatedinstructions, microcode, firmware instructions, status setting data, orsource code or object code written in any combination of one or moreprogramming languages, including object-oriented programming languagessuch as Smalltalk, C++, etc., as well as conventional proceduralprogramming languages such as the “C” language or similar programminglanguages. The computer-readable program instructions can be completelyexecuted on a user's computer, partially executed on a user's computer,executed as a separate software package, partially executed on a user'scomputer and partially executed on a remote computer, or completelyexecuted on a remote computer or a server. In the case where a remotecomputer is involved, the remote computer may be connected to a user'scomputer through any type of networks, including an LAN or a WAN, or maybe connected to an external computer, e.g., connected through theInternet by using an Internet service provider. In some embodiments, anelectronic circuit, such as a programmable logic circuit, a fieldprogrammable gate array (FPGA), or a programmable logic array (PLA), maybe customized by utilizing status information of computer-readableprogram instructions. The electronic circuit may execute thecomputer-readable program instructions to implement various aspects ofthe present disclosure.

Various aspects of the present disclosure are described herein withreference to flowcharts and/or block diagrams of the method,apparatus/system, and computer program product according to theembodiments of the present disclosure. It should be understood that eachblock in the flowcharts and/or the block diagrams and combinations ofthe blocks in the flowcharts and/or the block diagrams may beimplemented by computer-readable program instructions.

The computer-readable program instructions may be provided to aprocessing unit of a general-purpose computer, a special-purposecomputer, or other programmable data processing apparatuses, therebyproducing a machine, such that when these instructions are executed bythe processing unit of the computer or other programmable dataprocessing apparatuses, an apparatus for implementing functions/actionsspecified in one or more blocks in the flowcharts and/or the blockdiagrams is generated. The computer-readable program instructions mayalso be stored in the computer-readable storage medium. The instructionsenable a computer, a programmable data processing apparatus, and/orother devices to operate in a specific manner, so that thecomputer-readable medium storing the instructions includes an article ofmanufacture that includes instructions for implementing various aspectsof functions/actions specified in one or more blocks in the flowchartsand/or the block diagrams.

The computer-readable program instructions may also be loaded onto acomputer, other programmable data processing apparatuses, or otherdevices, such that a series of operational steps are performed on thecomputer, other programmable data processing apparatuses, or otherdevices to produce a computer-implemented process. Thus, theinstructions executed on the computer, other programmable dataprocessing apparatuses, or other devices implement the functions/actionsspecified in one or more blocks in the flowcharts and/or the blockdiagrams.

The flowcharts and block diagrams in the accompanying drawingsillustrate architectures, functions, and operations of possibleimplementations of systems, methods, and computer program productsaccording to multiple embodiments of the present disclosure. In thisregard, each block in the flowcharts or block diagrams can represent amodule, a program segment, or a portion of an instruction that includesone or more executable instructions for implementing specified logicalfunctions. In some alternative implementations, functions labeled in theblocks may occur in an order different from that labeled in theaccompanying drawings. For example, two successive blocks may actuallybe performed basically in parallel, or they may be performed in anopposite order sometimes, depending on the functions involved. It shouldalso be noted that each block in the block diagrams and/or flowchartsand a combination of blocks in the block diagrams and/or flowcharts canbe implemented using a dedicated hardware-based system for executingspecified functions or actions, or can be implemented using acombination of dedicated hardware and computer instructions.

Various embodiments of the present disclosure have been described above.The foregoing description is illustrative rather than exhaustive, and isnot limited to the disclosed embodiments. Multiple modifications andvariations will be apparent to those skilled in the art withoutdeparting from the scope and spirit of the illustrated variousembodiments. The selection of terms as used herein is intended to bestexplain the principles and practical applications of the variousembodiments or the technical improvements to technologies on the market,and to otherwise enable persons of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method for processing data, comprising:loading, at a switch and in response to receipt of a model loadingrequest from a terminal device, a data processing model specified in themodel loading request; acquiring model parameters of the data processingmodel from the terminal device; and processing, in response to receiptof to-be-processed data from the terminal device, the data using thedata processing model based on the model parameters.
 2. The methodaccording to claim 1, wherein loading the data processing modelcomprises: acquiring an identifier of the data processing model in themodel loading request; selecting, based on the identifier, the dataprocessing model from a predetermined data processing model set; andloading the selected data processing model.
 3. The method according toclaim 1, wherein acquiring the model parameters comprises: determining,based on a content label of a packet received from the terminal device,whether the packet comprises model parameters related to the dataprocessing model; and acquiring the model parameters from the packetaccording to a determination that the packet comprises the modelparameters.
 4. The method according to claim 3, wherein the dataprocessing model is a neural network model, and acquiring the modelparameters from the packet comprises: determining a parameter sizecorresponding to each processing layer of the neural network model; andacquiring model parameters corresponding to each processing layer fromthe packet according to the parameter size.
 5. The method according toclaim 1, further comprising: determining, based on a content label of apacket received from the terminal device, whether the packet comprisesdata to be processed by the data processing model; and determining,according to a determination that the packet comprises data to beprocessed by the data processing model, that the to-be-processed data isreceived.
 6. The method according to claim 1, wherein the dataprocessing model is a neural network model, and processing the datacomprises: processing, based on model parameters corresponding to eachprocessing layer of the neural network model, the data using the neuralnetwork model.
 7. The method according to claim 1, further comprising atleast one of the following: sending, in response to receipt of adestination address from the terminal device, a processing result forthe data based on the destination address; and sending the processingresult for the data to the terminal device.
 8. A method for processingdata, comprising: sending, at a terminal device, a model loading requestto a switch, a data processing model to be loaded by the switch beingspecified in the model loading request; sending model parameters of thedata processing model to the switch; and sending to-be-processed data tothe switch, such that the to-be-processed data is processed at theswitch based on the data processing model.
 9. The method according toclaim 8, further comprising: receiving a processing result for the datafrom the switch.
 10. The method according to claim 8, wherein sendingthe model parameters to the switch comprises: including the modelparameters in a packet to be sent; setting a content label of the packetas indicating that the packet comprises the model parameters; andsending the packet to the switch.
 11. The method according to claim 8,wherein sending to-be-processed data to the switch comprises: includingthe to-be-processed data in a packet to be sent; setting a content labelof the packet as indicating that the packet comprises theto-be-processed data; and sending the packet to the switch.
 12. Aswitch, comprising: a processor; and a memory storing computer programinstructions, wherein the processor runs the computer programinstructions in the memory to control an electronic device to performactions comprising: loading, in response to receipt of a model loadingrequest from a terminal device, a data processing model specified in themodel loading request; acquiring model parameters of the data processingmodel from the terminal device; and processing, in response to receiptof to-be-processed data from the terminal device, the data using thedata processing model based on the model parameters.
 13. The switchaccording to claim 12, wherein loading the data processing modelcomprises: acquiring an identifier of the data processing model in themodel loading request; selecting, based on the identifier, the dataprocessing model from a predetermined data processing model set; andloading the selected data processing model.
 14. The switch according toclaim 12, wherein acquiring the model parameters comprises: determining,based on a content label of a packet received from the terminal device,whether the packet comprises model parameters related to the dataprocessing model; and acquiring the model parameters from the packetaccording to a determination that the packet comprises the modelparameters.
 15. The switch according to claim 14, wherein the dataprocessing model is a neural network model, and acquiring the modelparameters from the packet comprises: determining a parameter sizecorresponding to each processing layer of the neural network model; andacquiring model parameters corresponding to each processing layer fromthe packet according to the parameter size.
 16. The switch according toclaim 12, wherein the actions further comprise: determining, based on acontent label of a packet received from the terminal device, whether thepacket comprises data to be processed by the data processing model; anddetermining, according to a determination that the packet comprises datato be processed by the data processing model, that the to-be-processeddata is received.
 17. The switch according to claim 12, wherein the dataprocessing model is a neural network model, and processing the datacomprises: processing, based on model parameters corresponding to eachprocessing layer of the neural network model, the data using the neuralnetwork model.
 18. The switch according to claim 12, wherein the actionsfurther comprise at least one of the following: sending, in response toreceipt of a destination address from the terminal device, a processingresult for the data based on the destination address; and sending theprocessing result for the data to the terminal device.
 19. A computerprogram product tangibly stored on a non-volatile computer-readablemedium and comprising machine-executable instructions, wherein themachine-executable instructions, when executed, cause a machine toperform the method of claim
 1. 20. A computer program product tangiblystored on a non-volatile computer-readable medium and comprisingmachine-executable instructions, wherein the machine-executableinstructions, when executed, cause a machine to perform the method ofclaim 8.